# 3D Dense Face Alignment via Graph Convolution Networks

arXiv: https://arxiv.org/abs/1904.05562 · HTML: https://mlsanity.com/paper/1904.05562/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction](https://mlsanity.com/paper/2107.12512/) (2021)
- [Instant Multi-View Head Capture through Learnable Registration](https://mlsanity.com/paper/2306.07437/) (2023)
- [FNeVR: Neural Volume Rendering for Face Animation](https://mlsanity.com/paper/2209.10340/) (2022)
- [Self-Supervised 3D Face Reconstruction via Conditional Estimation](https://mlsanity.com/paper/2110.04800/) (2021)
- [Learning Feature Aggregation for Deep 3D Morphable Models](https://mlsanity.com/paper/2105.02173/) (2021)
- [Parameter Efficient Local Implicit Image Function Network for Face Segmentation](https://mlsanity.com/paper/2303.15122/) (2023)
- [SPARK: Self-supervised Personalized Real-time Monocular Face Capture](https://mlsanity.com/paper/2409.07984/) (2024)
- [Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images](https://mlsanity.com/paper/2109.02288/) (2021)
- [Generating Diverse 3D Reconstructions from a Single Occluded Face Image](https://mlsanity.com/paper/2112.00879/) (2021)
- [Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence](https://mlsanity.com/paper/2605.01450/) (2026)
- [High-Fidelity 3D Facial Avatar Synthesis with Controllable Fine-Grained Expressions](https://mlsanity.com/paper/2603.14781/) (2026)

## Abstract (arXiv)

Recently, 3D face reconstruction and face alignment tasks are gradually combined into one task: 3D dense face alignment. Its goal is to reconstruct the 3D geometric structure of face with pose information. In this paper, we propose a graph convolution network to regress 3D face coordinates. Our method directly performs feature learning on the 3D face mesh, where the geometric structure and details are well preserved. Extensive experiments show that our approach gains superior performance over state-of-the-art methods on several challenging datasets.